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At least 163 records · Page 9

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DOE OSTI2026

Robust Heat-Flux Sensors for Coal-Fired Boiler Extreme Environments

In this project, robust heat-flux measurement systems were developed. The heat-flux sensors utilize thermoelectric effects to directly transduce the heat-flux inputs to analog electrical voltage signals. They were constructed from dedicated materials that can withstand temperatures of at least 1000°C and maintain adequate performance at these conditions for prolonged periods of time. The proposed approaches took into account numerous considerations, including system cost, sensor head resilience, sensor footprint, data accuracy, response time, and maintenance requirements. Through modern thermoelectric materials design, methodical materials selection and rigorous testing in materials characterization labs and medium-scale fire research facilities, we have demonstrated functioning laboratory prototypes, upon which one could base industrial heat-flux sensing platforms capable of operating in the challenging high-temperature, corrosive environments of the boilers of coal-fired power plants. A distributed sensor array for heat-flux measurements throughout the furnace water-wall, the superheater area and the economizer coils can provide critical data for the power plant control systems to increase efficiency, improve safety and reduce down times. For example, the combined heat-flux sensor/control systems can contribute to the optimization of burner and boiler operations under flexible loads, the optimization of heat-exchange conditions and overall reduction of heat rate and emissions, the prediction of imminent overheating conditions, and the optimization of the soot-blowing protocols.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

THz Radar Observations of Hydrometeors in a Spray Chamber

Abstract A THz radar, with its wide bandwidth, is capable of high‐resolution imaging down to the centimeter scale. In this study, a THz radar is applied to detect hydrometeors generated in a spray chamber. The observed backscattering signals show fluctuations at centimeter scales, indicating various hydrometeor distribution patterns along the radar beam. A co‐located High‐Speed Imaging (HSI) sensor is used to measure the Drop Size Distributions (DSD) in the spray chamber. The radar sampling beam is well aligned with the HSI probes, allowing an objective comparison between the remote sensing and in situ observations. In this study, the observed radar power is compared with the power estimated from the HSI measurements. Results show great consistency, with power difference smaller than 0.5 dB. This study demonstrates the feasibility and great potential of using a THz radar for ultra‐high‐resolution observations of clouds in a laboratory facility, and in the real atmosphere.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Remote Sensing Improves Multi‐Hazard Flooding and Extreme Heat Detection by Fivefold Over Current Estimates

The co‐occurrence of multiple hazards is of growing concern globally as the frequency and magnitude of extreme climate events increases. Despite studies examining the spatial distribution of such events, there has been little work in examining if all relevant life threatening and damaging hazards are captured in existing hazard databases and by common hazard metrics. For example, local/regional flash flooding events are seldom captured by optical satellite instruments and are subsequently excluded from global hazard databases. Similarly, the heat hazard definitions most frequently used in multi‐hazard studies inherently fail to capture events that are life‐threatening but climatologically within an expected range. Our goal is to determine the potential for increasing multi‐hazard event detection capabilities by inferring additional hazard footprints from widely accessible satellite data. We use daily precipitation and temperature satellite data to develop an open‐source framework that infers additional hazard footprints that are not included in traditional methods. With the state of Texas as our study area, we detected 2.5 times as many flood hazards, equivalent to $320 million in property and crop damages. Furthermore, our expanded heat hazard definition increases the impacted area by 56.6%, equivalent to 91.5 million km 2 over an 18 year period. Increasing hazard detection capabilities and expanding existing definitions of hazards using daily satellite data increases the temporal and spatial resolutions at which multi‐hazard events are detected. Having more complete data sets of all relevant hazard extents improves our ability to track global trends and more accurately determine the magnitude of hazard exposure inequities.

equity↗

DOE OSTI2025

Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulations for acceptable accuracy. Surrogate models provide a means of circumventing the high computational expense of sampling such complex models. In particular, polynomial chaos expansions (PCEs) have been successfully used for uncertainty quantification studies of deterministic models where the dominant source of uncertainty is parametric. We discuss an extension to conventional PCE surrogate modeling to enable surrogate construction for stochastic computational models that have intrinsic noise in addition to parametric uncertainty. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations, which are evaluated via kernel density estimation of the associated conditional cumulative distributions. Furthermore, we extend the construction to random field data via the Karhunen–Loève expansion. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model which quantifies the intrinsic noise contribution to the overall model output variance. Additionally, the resulting joint PCE is generative in the sense that it allows generating random realizations at any input parameter setting that are statistically approximately equivalent to realizations from the underlying stochastic model. The method is demonstrated on a chemical catalysis example model and a synthetic example controlled by a parameter that enables a switch from unimodal to bimodal response distributions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Enhanced imaging of electronic hot spots using quantum squeezed light

Detecting electronic hot spots is important for understanding the heat dissipation and thermal management of electronic and semiconductor devices. Optical thermoreflective imaging is being used to perform precise temporal and spatial imaging of heat on wires and semiconductor materials. We apply quantum squeezed light to perform thermoreflective imaging on micro-wires, surpassing the shot-noise limit of classical approaches. We obtain a far-field temperature sensing accuracy of 42 mK after 50 ms of averaging and show that a 256×256 pixel image can be constructed with such sensitivity in 10 min. We can further obtain single-shot temperature sensing of 1.6 K after only 10 μs of averaging, enabling a dynamical study of heat dissipation. Not only do the quantum images provide accurate spatiotemporal information about heat distribution but also the measure of quantum correlation provides additional information, inaccessible by classical techniques, which can lead to a better understanding of the dynamics. We apply the technique to both aluminum and niobium microwires and discuss the applications of the technique in studying electron dynamics at low temperatures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Xyce™ Parallel Electronic Simulator Users' Guide (V.7.9)

This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: • Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). This includes support for most popular parallel and serial computers. • A differential-algebraic-equation (DAE) formulation, which better isolates the device model package from solver algorithms. This allows one to develop new types of analysis without requiring the implementation of analysis-specific device models. • Device models that are specifically tailored to meet Sandia’s needs, including some radiation-aware devices (for Sandia users only). • Object-oriented code design and implementation using modern coding practices. Xyce is a parallel code in the most general sense of the phrase — a message passing parallel implementation — which allows it to run efficiently a wide range of computing platforms. These include serial, shared-memory and distributed-memory parallel platforms. Attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows.

42 ENGINEERING↗

DOE OSTI2025

Xyce™ Parallel Electronic Simulator Users’ Guide, Version 7.10

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Imaging of a van der Waals spin-orbit torque system using spin ensembles in hBN

Recently, optically active spin defects embedded in two-dimensional (2D) van der Waals (vdW) crystals have emerged as a transformative quantum sensing platform to explore cutting-edge materials science. Taking advantage of excellent solid-state integrability, this new class of spin defects can be readily arranged in nanoscale proximity to target materials, showing great promise for realizing in-situ quantum sensing of microscopic spin and charge behaviors in vdW heterostructures. Here we report hexagonal boron nitride-based quantum imaging of field-free deterministic magnetic switching and electric current distributions in an all-vdW spin-orbit torque (SOT) system. By visualizing variations of nanoscale magnetic stray field profile of room-temperature 2D magnet Fe 3 GaTe 2 under different SOT conditions, we show how the magnetic switching evolves from deterministic to stochastic behavior due to the interplay between spin orientations, anisotropy and Joule heating. Micromagnetic simulations rationalize our results well, revealing the role of field-like SOT in inhibiting thermal fluctuation driven stochastic switching and chaotic multi-domain competition. This understanding, which is otherwise difficult to access by conventional transport measurements, offers valuable insights into material design, testing, and performance evaluation of next-generation vdW spintronic devices.

Imaging techniques↗

DOE OSTI2026

Shaping with water: linking moisture perception to development in plant roots

Water is the most limiting resource for plant growth and development. Heterogeneity in the environmental distribution of water requires plants to direct root growth toward water and to avoid investing resources in areas that lack water. Roots use hydrosignaling pathways—hydrotropism, hydropatterning, and xerobranching—to sense and respond to water availability. While molecular mechanisms of water perception remain unclear, recent studies suggest that organ-level processes using proxies like ethylene help detect spatial water patterns. This review summarizes advances in hydrosignaling and identifies key knowledge gaps to address how plants sense water. Understanding these processes will guide strategies to improve root water capture for sustainable agriculture.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Recommendations for developing, documenting, and distributing data products derived from NEON data

The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly-generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

Arid and semi-arid landscapes↗

DOE OSTI2024

Isotope engineering for spin defects in van der Waals materials

Abstract Spin defects in van der Waals materials offer a promising platform for advancing quantum technologies. Here, we propose and demonstrate a powerful technique based on isotope engineering of host materials to significantly enhance the coherence properties of embedded spin defects. Focusing on the recently-discovered negatively charged boron vacancy center ($${{{{{{{{\rm{V}}}}}}}}}_{{{{{{{{\rm{B}}}}}}}}}^{-}$$ V B − ) in hexagonal boron nitride (hBN), we grow isotopically purified h 10 B 15 N crystals. Compared to$${{{{{{{{\rm{V}}}}}}}}}_{{{{{{{{\rm{B}}}}}}}}}^{-}$$ V B − in hBN with the natural distribution of isotopes, we observe substantially narrower and less crowded$${{{{{{{{\rm{V}}}}}}}}}_{{{{{{{{\rm{B}}}}}}}}}^{-}$$ V B − spin transitions as well as extended coherence timeT 2 and relaxation timeT 1 . For quantum sensing,$${{{{{{{{\rm{V}}}}}}}}}_{{{{{{{{\rm{B}}}}}}}}}^{-}$$ V B − centers in our h 10 B 15 N samples exhibit a factor of 4 (2) enhancement in DC (AC) magnetic field sensitivity. For additional quantum resources, the individual addressability of the$${{{{{{{{\rm{V}}}}}}}}}_{{{{{{{{\rm{B}}}}}}}}}^{-}$$ V B − hyperfine levels enables the dynamical polarization and coherent control of the three nearest-neighbor 15 N nuclear spins. Our results demonstrate the power of isotope engineering for enhancing the properties of quantum spin defects in hBN, and can be readily extended to improving spin qubits in a broad family of van der Waals materials.

Science & Technology - Other Topics↗

DOE OSTI2025

Covariant Quantum Error-Correcting Codes with Metrological Entanglement Advantage

Here, we show that a subset of the basis for the irreducible representations of a tensor-product SU(2) rotation forms a covariant approximate quantum error-correcting code with transversal U(1) logical gates. Generalizing previous work on “thermodynamic codes” to general local spin and different irreducible representations using only properties of the angular momentum algebra, we obtain bounds on the code inaccuracy under generic noise on any known 𝑑 sites, under independent and identically distributed noise, and under heralded 𝑑-local erasures. We demonstrate that this family of codes protects a probe state with quantum Fisher information surpassing the standard quantum limit when the sensing parameter couples to the generator of the U(1) logical gate.

quantum error correction↗

DOE OSTI2024

Aerosol Vertical Turbulent Mass Flux Retrievals Through Novel Remote Sensing Algorithm

Abstract Integrated measurements of aerosol, radiation, cloud, and turbulent transport in the planetary boundary layer (PBL) are essential for understanding and modeling climate and air quality. Here, we developed a new technique for the identification of convective turbulent regions and deriving the vertical distribution of aerosol turbulent mass fluxes within PBL. The algorithm uses retrievals from coherent Doppler lidars and a high spectral resolution lidar. The technique was applied to study particle mass fluxes over 2 months (November–December 2020) during the campaign conducted at the DOE Atmospheric Radiation Measurement Southern Great Plains (SGP) site in Lamont, Oklahoma. The algorithm developed here is capable of continuously deriving vertically resolved (curtains) aerosol mass fluxes. Our data analysis shows that at the site, the 30‐min averaged fluxes at 135 m above the surface were mainly positive (upward) at ∼1 μg m −2 s −1 , suggesting that the surface is the primary source of the particle mass supplied to the boundary layer at the SGP site. Analyses of the individual case studies have revealed that not all the derived fluxes can be linked to surface emissions. Both positive and negative values in a range of ±5 μg m −2 s −1 can be caused by convective thermals interacting between the residual layer and the mixed layer and by rotation of the horizontal wind with the height. Large erroneous negative fluxes can also be caused by drizzling/precipitating clouds. We anticipate that the application of the current technique will lead to a more realistic representation of aerosol mass budgets and bidirectional mixing rates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Studying Open Quantum Systems Relevant to Chemistry on a Trapped-Ion Quantum Simulator (Final Technical Report)

This project advances the trapped-ion quantum simulator as a versatile platform for studying open quantum system phenomena. We aim to contribute to the emerging quantum simulation toolkits and enable simulation of nanoscale energy processes. Trapped-ion platforms offer unique capabilities: their vibrational motion can be precisely manipulated, measured, and coherently coupled to auxiliary qubits. The vibrational mode can function both as a highly sensitive quantum sensor and a programmable environment bath. Using this platform, we achieved three major outcomes. First, we demonstrated using the vibrational mode as an ultrasensitive probe for testing fundamental physics, including possible nonlinear quantum mechanics effects. Second, we established that these modes can act as controllable baths in which tunable noise and loss can enhance or modify energy-transfer dynamics, providing the experimental preparation toward studying mechanisms relevant to chemical reactions and light-harvesting systems. Third, by introducing controllable nonlinear gain and loss, we showed theoretically how simulations using trapped ions can model vibrationally-assisted energy transport in a non‐Hermitian quantum system comprising a chromophore dimer weakly coupled to a vibrational mode. Exploring the non‐Hermitian dynamics of the whole system including vibrations, we found that energy transfer accompanied by absorption of phonons from a vibrational mode can be significantly enhanced near an exceptional point. This theoretical work on simulation of energy transfer processes in driven non‐Hermitian quantum systems revealed an interesting novel path to study open quantum systems dynamics under conditions of gain and loss. We then further explored the benefits of controllable gain and loss with an experimental realization of quantum analogs of nonlinear oscillators, namely, the van der Pol oscillator. Here we observed mutual synchronization mediated by collective dissipation between two oscillators. In parallel, we explored related quantum networking protocols using the same trapped-ion platform, developing fast, high-fidelity schemes for distributing entanglement. Together, these achievements show that trapped-ion vibrational modes provide a highly programmable and high-fidelity platform for investigating complex dissipative quantum behavior, while enabling new approaches to remote quantum sensing, energy science, and nonlinear quantum dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Exploring the Feasibility of Modern Fiber Optics in Offshore Mooring Condition Monitoring

This project explored the feasibility of using fiber-optic sensing technologies for condition monitoring of offshore synthetic mooring lines used in marine renewable energy systems. The work focused on evaluating optical fiber materials, interrogation methods, and rope-embedment approaches capable of enabling distributed strain or elongation measurements along mooring lines.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

Electronic Spin Relaxation and Clustering in High-Pressure High-Temperature Synthesized Microcrystalline Diamond Particles with Reduced Nitrogen Content

The negatively charged nitrogen-vacancy (NV – ) color center in diamonds is widely studied because of numerous applications of this unique quantum system in sensing and quantum information sciences. While substitutional nitrogen is required to form the NV – centers in diamond, it also yields other paramagnetic defects─primarily the neutrally charged substitutional nitrogen centers (P1)─that decrease NV – spin coherence, which in turn degrades performance in applications. Herein, we investigate high-pressure high-temperature synthesized diamond microparticles (ca. 140–185 μm) having lower─ranging from 3 to 38 ppm─than the typical nitrogen content of type 1b diamond ( ca. 100 ppm and higher) typically used for the production of fluorescent diamond particles with NV – centers. A suite of electron paramagnetic resonance, optically detected magnetic resonance, and nuclear magnetic resonance methods are used to characterize spin properties of P1 and NV – centers in the particles. Upon decreasing the nitrogen content from 29 to 3 ppm, the ensemble NV – T 2 relaxation time increased by about 3-fold as measured directly in the Hahn Echo experiment at magnetic field of 1.2 T. Analysis of electronic relaxation of P1 centers revealed the existence of at least two distinct populations of P1 centers, consisting of fast and slower relaxing spins and allowed for an estimation of local concentrations. Even with <10 ppm nitrogen contents, the analysis indicated a highly heterogeneous distribution of P1 centers, suggesting the possibility of P1 spin clustering even at low nitrogen concentrations. The combined data demonstrate that the particles prepared from HPHT diamond with a low nitrogen content offer improved spin properties that are beneficial for NV – sensing applications.

Carbon↗

DOE OSTI2026

Microbial spies and bloggers: programming cells to convert environmental information into discernible signals

Microbes regulate their dynamic behaviors using the chemical and physical characteristics of their environment. The ability of microbes to continuously convert this physicochemical information into biochemical information and to use organic matter in the environment as a power source makes these organisms attractive as chassis for building sensors. However, most biosensors have severe limitations when considering applications in hard-to-image settings like soils, sediments, and wastewater. Emerging technologies at the interface of biomolecular design, microbiome engineering, and synthetic biology offer new tools to program cells and communities as biosensors for these settings. Here, in this review, we describe innovations in biosensor outputs that are enabling new applications in complex environments, including reporters that are read out using electrochemical, gas chromatography, hyperspectral imaging, and next-generation sequencing methods. We also discuss computational advances that are accelerating the diversification of sensing components by mining metagenomics data for new transcriptional regulators and by designing allosteric protein switches that directly regulate reporter outputs using analytes. We highlight emerging opportunities for programming undomesticated microbes in communities to function as distributed sensors in the environment. Finally, we discuss the need for responsible biosensor development and to modernize regulatory frameworks to support evidence-based assessment of environmental biosensors.

analyte↗